Release 2.0.4 (#125)

# What's Changed
* Noise Module (by @[email protected] in [Commit from May 13, 2024](e797af9)): Brings noise simulation features from IBM Qiskit.
* Rollback of IBM Eagle Acces (#116) because of unresolvable dependency issues with new noise module. These issues will be adressed in the upcoming major update in which this feature will be included again. Until then, if you want to use this feature, please move to version 2.0.3
* Maximum Indepent Set (MIS) Application (by @chris-van-den-oetelaar in #120): Implementation of full benchmarking pipeline for the MIS use case, including a mapping, interface and solver for neutral-atom devices
* Minor changes: Debugging of RandomClassicPVC (#124), added unit test for MIS application and fixed linting errors
Full changelog: v2.0.3...v2.0.4

.settings/requirements_full.txt

@@ -23,8 +23,10 @@ more-itertools==9.0.0
 qiskit-optimization==0.5.0
 pyqubo==1.4.0
 dwave_networkx==0.8.13
-qiskit==0.40.0
+qiskit==0.45.0
 pandas==1.5.2
 cma==3.3.0
 tensorboard==2.13.0
 tensorboardX==2.6.2
+pulser==0.16.0
+qiskit-aer==0.11.2

src/Installer.py

@@ -44,7 +44,8 @@ def __init__(self):
             {"name": "SAT", "class": "SAT", "module": "modules.applications.optimization.SAT.SAT"},
             {"name": "TSP", "class": "TSP", "module": "modules.applications.optimization.TSP.TSP"},
             {"name": "GenerativeModeling", "class": "GenerativeModeling",
-             "module": "modules.applications.QML.generative_modeling.GenerativeModeling"}
+             "module": "modules.applications.QML.generative_modeling.GenerativeModeling"},
+            {"name": "MIS", "class": "MIS", "module": "modules.applications.optimization.MIS.MIS"},
         ]
 
         self.core_requirements = [

src/main.py

@@ -21,7 +21,6 @@
 import yaml
 
 from Installer import Installer
-from Plotter import Plotter
 from utils import _expand_paths
 from utils_mpi import MPIStreamHandler, MPIFileHandler, get_comm
 
@@ -157,6 +156,8 @@ def handle_benchmark_run(args: argparse.Namespace) -> None:
     :rtype: None
     """
     from BenchmarkManager import BenchmarkManager  # pylint: disable=C0415
+    from Plotter import Plotter  # pylint: disable=C0415
+
     benchmark_manager = BenchmarkManager(fail_fast=args.failfast)
 
     if args.summarize:

src/modules/applications/QML/generative_modeling/data/data_handler/DataHandler.py

@@ -14,6 +14,7 @@
 
 import pickle
 import os
+from qiskit import qpy
 
 import numpy as np
 import pandas as pd
@@ -25,7 +26,7 @@
 
 class DataHandler(Core, ABC):
     """
-    The task of the DataHandler module is to translate the application’s data 
+    The task of the DataHandler module is to translate the application’s data
     and problem specification into preproccesed format.
     """
 
@@ -150,6 +151,10 @@ def postprocess(self, input_data: dict, config: dict, **kwargs):
             with open(f"{store_dir_iter}/training_results-{kwargs['rep_count']}.pkl", 'wb') as f:
                 pickle.dump(input_data, f)
 
+            if "circuit_transpiled" in list(input_data.keys()):
+                with open(f"{store_dir_iter}/transpiled_circuit_{kwargs['rep_count']}.qpy", 'wb') as f:
+                    qpy.dump(input_data.pop("circuit_transpiled"), f)
+
             # Save variables transformed space
             if "Transformation" in list(input_data.keys()):
                 self.metrics.add_metric_batch({"KL_best": input_data["KL_best_transformed"]})
@@ -164,7 +169,7 @@ def postprocess(self, input_data: dict, config: dict, **kwargs):
     @abstractmethod
     def data_load(self, gen_mod: dict, config: dict) -> dict:
         """
-        Helps to ensure that the model can effectively learn the underlying 
+        Helps to ensure that the model can effectively learn the underlying
         patterns and structure of the data, and produce high-quality outputs.
 
         :param gen_mod: dictionary with collected information of the previous modules
@@ -207,12 +212,12 @@ def evaluate(self, solution: any) -> (dict, float):
     @staticmethod
     def tb_to_pd(logdir: str, rep: str) -> None:
         """
-        Converts TensorBoard event files in the specified log directory 
+        Converts TensorBoard event files in the specified log directory
         into a pandas DataFrame and saves it as a pickle file.
 
         :param logdir: path to the log directory containing TensorBoard event files
-        :type logdir: str 
-        
+        :type logdir: str
+
         """
         event_acc = EventAccumulator(logdir)
         event_acc.Reload()